Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f435a8a60f0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f435765cc88>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='inputs_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        
        alpha = 0.2
        keep_prob = 0.9
        
        # Input layer
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, 
                              padding='same', 
                              kernel_initializer=tf.contrib.layers.xavier_initializer()) # Use xavier initializer
        relu1 = tf.maximum(alpha * x1, x1)
        relu1 = tf.nn.dropout(relu1, keep_prob)
        
        # Conv layer
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, 
                              padding='same', 
                              kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        relu2 = tf.nn.dropout(relu2, keep_prob)
        
        # Conv layer
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, 
                              padding='same', 
                              kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        relu3 = tf.nn.dropout(relu3, keep_prob)
        
        # Flat layer
        unit_num = relu3.get_shape()[1] * relu3.get_shape()[2] * relu3.get_shape()[3]
        flat = tf.reshape(relu3, (-1, int(unit_num)))
        
        # Logits
        logits = tf.layers.dense(flat, 1)
        
        # Output
        output = tf.sigmoid(logits)

    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):
        
        alpha = 0.2
        keep_prob = 0.9
        
        # Fully-connected layer
        x1 = tf.layers.dense(z, 4*4*512)
        
        # Reshape
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = tf.maximum(alpha * x1, x1)
        relu1 = tf.nn.dropout(relu1, keep_prob)
        
        # Conv_t layer
        x2 = tf.layers.conv2d_transpose(relu1, 256, 5, strides=2, 
                                        padding='same', 
                                        kernel_initializer=tf.contrib.layers.xavier_initializer())
        x2 = tf.image.resize_nearest_neighbor(x2, (7, 7))
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = tf.maximum(alpha * x2, x2)
        relu2 = tf.nn.dropout(relu2, keep_prob)
        
        # Conv_t layer
        x3 = tf.layers.conv2d_transpose(relu2, 128, 5, strides=2, 
                                        padding='same', 
                                        kernel_initializer=tf.contrib.layers.xavier_initializer())
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        relu3 = tf.maximum(alpha * x3, x3)
        relu3 = tf.nn.dropout(relu3, keep_prob)
        
        # Logits
        logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5, strides=2, 
                                            padding='same', 
                                            kernel_initializer=tf.contrib.layers.xavier_initializer())
        
        # Output
        output = tf.tanh(logits)

    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
import numpy as np

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
#     d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
#                                                                          labels=tf.ones_like(d_model_real) * (1 - 0.1)))
#     d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
#                                                                          labels=tf.zeros_like(d_model_fake)))
#     g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
#                                                                     labels=tf.ones_like(d_model_fake)))

    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                                         labels=tf.ones_like(d_model_real) * (1 - 0.1) + np.random.uniform(-0.05, 0.05)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                                         labels=tf.zeros_like(d_model_fake) + np.random.uniform(0.0, 0.1)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                                    labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get variables
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # Optimizer
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_Rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    print_num = 10
    show_num = 100
    
    show_images_num = 25
    
    # Get images information
    image_width = data_shape[1]
    image_height = data_shape[2]
    image_channels = data_shape[3]
        
    # Get inputs
    inputs_real, inputs_z, learning_rate = model_inputs(image_width, image_height, image_channels, z_dim)
    
    # Get losses
    d_loss, g_loss = model_loss(inputs_real, inputs_z, image_channels)
    
    # Get optimizers
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_Rate, beta1)
    
#     saver = tf.train.Saver()
    
    ii = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        for epoch_i in range(epoch_count):
            
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                ii += 1
                    
                # Scale image inputs to (-1, 1)
                batch_images = batch_images * 2
                
                # Sample random noise
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images, 
                                                     inputs_z: batch_z,
                                                     learning_rate: learning_Rate})
                _ = sess.run(g_train_opt, feed_dict={inputs_real: batch_images, 
                                                     inputs_z: batch_z,
                                                     learning_rate: learning_Rate})
                
                if ii % print_num == 0:
                    # At losses
                    d_cost = d_loss.eval({inputs_z: batch_z, inputs_real: batch_images})
                    g_cost = g_loss.eval({inputs_z: batch_z})
                    
                    print("Epoch: {}/{} |".format(epoch_i+1, epoch_count),
                          "Steps: {:>4} |".format(ii),
                          "Discriminator Loss: {:.4f} |".format(d_cost),
                          "Generator Loss: {:.4f}".format(g_cost))
                
                if ii % show_num == 0:
                    show_generator_output(sess, 
                                          show_images_num, 
                                          inputs_z, 
                                          image_channels, 
                                          data_image_mode)
            
#         saver.save(sess, './checkpoints/generator.ckpt')          

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch: 1/2 | Steps:   10 | Discriminator Loss: 0.7222 | Generator Loss: 3.5033
Epoch: 1/2 | Steps:   20 | Discriminator Loss: 0.7716 | Generator Loss: 5.5186
Epoch: 1/2 | Steps:   30 | Discriminator Loss: 1.2066 | Generator Loss: 1.3344
Epoch: 1/2 | Steps:   40 | Discriminator Loss: 1.4097 | Generator Loss: 1.0474
Epoch: 1/2 | Steps:   50 | Discriminator Loss: 1.4976 | Generator Loss: 0.6097
Epoch: 1/2 | Steps:   60 | Discriminator Loss: 1.1781 | Generator Loss: 1.5273
Epoch: 1/2 | Steps:   70 | Discriminator Loss: 1.3298 | Generator Loss: 1.1719
Epoch: 1/2 | Steps:   80 | Discriminator Loss: 1.1563 | Generator Loss: 0.8641
Epoch: 1/2 | Steps:   90 | Discriminator Loss: 0.8462 | Generator Loss: 1.3799
Epoch: 1/2 | Steps:  100 | Discriminator Loss: 0.7693 | Generator Loss: 1.8221
Epoch: 1/2 | Steps:  110 | Discriminator Loss: 1.0718 | Generator Loss: 3.3798
Epoch: 1/2 | Steps:  120 | Discriminator Loss: 1.1632 | Generator Loss: 0.8773
Epoch: 1/2 | Steps:  130 | Discriminator Loss: 1.0469 | Generator Loss: 1.4574
Epoch: 1/2 | Steps:  140 | Discriminator Loss: 0.7778 | Generator Loss: 1.7375
Epoch: 1/2 | Steps:  150 | Discriminator Loss: 0.8836 | Generator Loss: 1.8074
Epoch: 1/2 | Steps:  160 | Discriminator Loss: 0.8972 | Generator Loss: 1.3665
Epoch: 1/2 | Steps:  170 | Discriminator Loss: 1.1105 | Generator Loss: 3.2291
Epoch: 1/2 | Steps:  180 | Discriminator Loss: 0.9458 | Generator Loss: 1.8175
Epoch: 1/2 | Steps:  190 | Discriminator Loss: 0.8730 | Generator Loss: 1.6301
Epoch: 1/2 | Steps:  200 | Discriminator Loss: 1.1619 | Generator Loss: 1.3068
Epoch: 1/2 | Steps:  210 | Discriminator Loss: 0.9032 | Generator Loss: 2.2908
Epoch: 1/2 | Steps:  220 | Discriminator Loss: 0.8862 | Generator Loss: 2.0158
Epoch: 1/2 | Steps:  230 | Discriminator Loss: 0.8391 | Generator Loss: 1.8182
Epoch: 1/2 | Steps:  240 | Discriminator Loss: 1.0489 | Generator Loss: 1.0058
Epoch: 1/2 | Steps:  250 | Discriminator Loss: 1.3305 | Generator Loss: 0.7878
Epoch: 1/2 | Steps:  260 | Discriminator Loss: 1.2658 | Generator Loss: 1.1583
Epoch: 1/2 | Steps:  270 | Discriminator Loss: 1.2539 | Generator Loss: 1.2446
Epoch: 1/2 | Steps:  280 | Discriminator Loss: 1.1673 | Generator Loss: 1.1339
Epoch: 1/2 | Steps:  290 | Discriminator Loss: 1.1038 | Generator Loss: 1.1419
Epoch: 1/2 | Steps:  300 | Discriminator Loss: 1.1470 | Generator Loss: 1.1369
Epoch: 1/2 | Steps:  310 | Discriminator Loss: 1.1512 | Generator Loss: 0.8010
Epoch: 1/2 | Steps:  320 | Discriminator Loss: 1.2793 | Generator Loss: 0.8682
Epoch: 1/2 | Steps:  330 | Discriminator Loss: 1.2904 | Generator Loss: 1.2099
Epoch: 1/2 | Steps:  340 | Discriminator Loss: 1.1554 | Generator Loss: 1.6850
Epoch: 1/2 | Steps:  350 | Discriminator Loss: 1.1062 | Generator Loss: 1.4504
Epoch: 1/2 | Steps:  360 | Discriminator Loss: 1.2810 | Generator Loss: 1.8142
Epoch: 1/2 | Steps:  370 | Discriminator Loss: 1.1102 | Generator Loss: 1.0440
Epoch: 1/2 | Steps:  380 | Discriminator Loss: 1.2719 | Generator Loss: 0.7382
Epoch: 1/2 | Steps:  390 | Discriminator Loss: 1.1453 | Generator Loss: 0.9803
Epoch: 1/2 | Steps:  400 | Discriminator Loss: 1.2893 | Generator Loss: 0.7604
Epoch: 1/2 | Steps:  410 | Discriminator Loss: 0.9107 | Generator Loss: 1.5576
Epoch: 1/2 | Steps:  420 | Discriminator Loss: 1.1091 | Generator Loss: 1.1554
Epoch: 1/2 | Steps:  430 | Discriminator Loss: 1.1436 | Generator Loss: 0.8974
Epoch: 1/2 | Steps:  440 | Discriminator Loss: 1.3340 | Generator Loss: 0.9576
Epoch: 1/2 | Steps:  450 | Discriminator Loss: 0.9665 | Generator Loss: 1.2755
Epoch: 1/2 | Steps:  460 | Discriminator Loss: 1.0198 | Generator Loss: 1.0659
Epoch: 1/2 | Steps:  470 | Discriminator Loss: 1.0586 | Generator Loss: 1.0082
Epoch: 1/2 | Steps:  480 | Discriminator Loss: 1.0223 | Generator Loss: 0.8584
Epoch: 1/2 | Steps:  490 | Discriminator Loss: 1.0440 | Generator Loss: 0.8525
Epoch: 1/2 | Steps:  500 | Discriminator Loss: 1.1972 | Generator Loss: 1.1386
Epoch: 1/2 | Steps:  510 | Discriminator Loss: 1.2439 | Generator Loss: 0.7722
Epoch: 1/2 | Steps:  520 | Discriminator Loss: 1.1572 | Generator Loss: 1.2832
Epoch: 1/2 | Steps:  530 | Discriminator Loss: 1.1723 | Generator Loss: 0.7578
Epoch: 1/2 | Steps:  540 | Discriminator Loss: 1.2285 | Generator Loss: 0.6813
Epoch: 1/2 | Steps:  550 | Discriminator Loss: 1.0981 | Generator Loss: 0.9578
Epoch: 1/2 | Steps:  560 | Discriminator Loss: 0.9331 | Generator Loss: 1.2095
Epoch: 1/2 | Steps:  570 | Discriminator Loss: 1.1396 | Generator Loss: 1.2813
Epoch: 1/2 | Steps:  580 | Discriminator Loss: 1.0408 | Generator Loss: 1.0835
Epoch: 1/2 | Steps:  590 | Discriminator Loss: 1.0457 | Generator Loss: 1.2746
Epoch: 1/2 | Steps:  600 | Discriminator Loss: 1.0405 | Generator Loss: 1.1647
Epoch: 1/2 | Steps:  610 | Discriminator Loss: 1.1011 | Generator Loss: 0.8683
Epoch: 1/2 | Steps:  620 | Discriminator Loss: 1.0437 | Generator Loss: 1.0730
Epoch: 1/2 | Steps:  630 | Discriminator Loss: 0.9743 | Generator Loss: 1.3863
Epoch: 1/2 | Steps:  640 | Discriminator Loss: 1.0575 | Generator Loss: 1.4066
Epoch: 1/2 | Steps:  650 | Discriminator Loss: 1.0494 | Generator Loss: 0.8491
Epoch: 1/2 | Steps:  660 | Discriminator Loss: 1.0689 | Generator Loss: 1.5856
Epoch: 1/2 | Steps:  670 | Discriminator Loss: 1.1384 | Generator Loss: 0.7347
Epoch: 1/2 | Steps:  680 | Discriminator Loss: 1.0592 | Generator Loss: 1.6479
Epoch: 1/2 | Steps:  690 | Discriminator Loss: 0.9629 | Generator Loss: 1.2282
Epoch: 1/2 | Steps:  700 | Discriminator Loss: 1.0525 | Generator Loss: 0.9083
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Epoch: 2/2 | Steps: 3240 | Discriminator Loss: 0.7481 | Generator Loss: 1.8762
Epoch: 2/2 | Steps: 3250 | Discriminator Loss: 0.7690 | Generator Loss: 1.5578
Epoch: 2/2 | Steps: 3260 | Discriminator Loss: 0.9200 | Generator Loss: 1.1293
Epoch: 2/2 | Steps: 3270 | Discriminator Loss: 0.8489 | Generator Loss: 1.3355
Epoch: 2/2 | Steps: 3280 | Discriminator Loss: 1.0135 | Generator Loss: 0.9851
Epoch: 2/2 | Steps: 3290 | Discriminator Loss: 0.9211 | Generator Loss: 1.3799
Epoch: 2/2 | Steps: 3300 | Discriminator Loss: 0.9247 | Generator Loss: 1.1951
Epoch: 2/2 | Steps: 3310 | Discriminator Loss: 0.8572 | Generator Loss: 1.3725
Epoch: 2/2 | Steps: 3320 | Discriminator Loss: 0.8923 | Generator Loss: 1.6480
Epoch: 2/2 | Steps: 3330 | Discriminator Loss: 0.8312 | Generator Loss: 1.0425
Epoch: 2/2 | Steps: 3340 | Discriminator Loss: 1.0273 | Generator Loss: 0.6788
Epoch: 2/2 | Steps: 3350 | Discriminator Loss: 0.8177 | Generator Loss: 1.3966
Epoch: 2/2 | Steps: 3360 | Discriminator Loss: 0.9356 | Generator Loss: 1.0557
Epoch: 2/2 | Steps: 3370 | Discriminator Loss: 0.9378 | Generator Loss: 0.9159
Epoch: 2/2 | Steps: 3380 | Discriminator Loss: 0.8154 | Generator Loss: 2.1019
Epoch: 2/2 | Steps: 3390 | Discriminator Loss: 0.8487 | Generator Loss: 1.2777
Epoch: 2/2 | Steps: 3400 | Discriminator Loss: 0.7441 | Generator Loss: 1.6125
Epoch: 2/2 | Steps: 3410 | Discriminator Loss: 1.2967 | Generator Loss: 1.7362
Epoch: 2/2 | Steps: 3420 | Discriminator Loss: 0.8744 | Generator Loss: 2.5387
Epoch: 2/2 | Steps: 3430 | Discriminator Loss: 0.7554 | Generator Loss: 1.3569
Epoch: 2/2 | Steps: 3440 | Discriminator Loss: 0.8125 | Generator Loss: 1.1717
Epoch: 2/2 | Steps: 3450 | Discriminator Loss: 0.6621 | Generator Loss: 1.9126
Epoch: 2/2 | Steps: 3460 | Discriminator Loss: 0.9412 | Generator Loss: 0.9596
Epoch: 2/2 | Steps: 3470 | Discriminator Loss: 0.9129 | Generator Loss: 1.1415
Epoch: 2/2 | Steps: 3480 | Discriminator Loss: 0.7429 | Generator Loss: 1.7582
Epoch: 2/2 | Steps: 3490 | Discriminator Loss: 0.7394 | Generator Loss: 2.0631
Epoch: 2/2 | Steps: 3500 | Discriminator Loss: 0.9728 | Generator Loss: 1.7481
Epoch: 2/2 | Steps: 3510 | Discriminator Loss: 0.8684 | Generator Loss: 1.5939
Epoch: 2/2 | Steps: 3520 | Discriminator Loss: 1.0300 | Generator Loss: 0.9169
Epoch: 2/2 | Steps: 3530 | Discriminator Loss: 0.7788 | Generator Loss: 1.3138
Epoch: 2/2 | Steps: 3540 | Discriminator Loss: 0.9082 | Generator Loss: 1.2427
Epoch: 2/2 | Steps: 3550 | Discriminator Loss: 1.0070 | Generator Loss: 0.7918
Epoch: 2/2 | Steps: 3560 | Discriminator Loss: 0.8468 | Generator Loss: 1.4333
Epoch: 2/2 | Steps: 3570 | Discriminator Loss: 0.8088 | Generator Loss: 1.0062
Epoch: 2/2 | Steps: 3580 | Discriminator Loss: 1.0076 | Generator Loss: 1.3100
Epoch: 2/2 | Steps: 3590 | Discriminator Loss: 0.8421 | Generator Loss: 1.9877
Epoch: 2/2 | Steps: 3600 | Discriminator Loss: 0.8461 | Generator Loss: 1.1673
Epoch: 2/2 | Steps: 3610 | Discriminator Loss: 1.1159 | Generator Loss: 1.0220
Epoch: 2/2 | Steps: 3620 | Discriminator Loss: 0.7429 | Generator Loss: 1.5948
Epoch: 2/2 | Steps: 3630 | Discriminator Loss: 0.9732 | Generator Loss: 0.8188
Epoch: 2/2 | Steps: 3640 | Discriminator Loss: 0.8633 | Generator Loss: 1.1760
Epoch: 2/2 | Steps: 3650 | Discriminator Loss: 0.8493 | Generator Loss: 1.9130
Epoch: 2/2 | Steps: 3660 | Discriminator Loss: 0.9602 | Generator Loss: 1.3373
Epoch: 2/2 | Steps: 3670 | Discriminator Loss: 0.8475 | Generator Loss: 1.6211
Epoch: 2/2 | Steps: 3680 | Discriminator Loss: 0.7270 | Generator Loss: 1.6113
Epoch: 2/2 | Steps: 3690 | Discriminator Loss: 0.8382 | Generator Loss: 1.3798
Epoch: 2/2 | Steps: 3700 | Discriminator Loss: 1.2300 | Generator Loss: 1.9021
Epoch: 2/2 | Steps: 3710 | Discriminator Loss: 0.9407 | Generator Loss: 1.1271
Epoch: 2/2 | Steps: 3720 | Discriminator Loss: 1.3225 | Generator Loss: 0.7039
Epoch: 2/2 | Steps: 3730 | Discriminator Loss: 0.7843 | Generator Loss: 1.6440
Epoch: 2/2 | Steps: 3740 | Discriminator Loss: 0.8943 | Generator Loss: 1.0197
Epoch: 2/2 | Steps: 3750 | Discriminator Loss: 1.0061 | Generator Loss: 1.7421

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch: 1/1 | Steps:   10 | Discriminator Loss: 0.8396 | Generator Loss: 1.4594
Epoch: 1/1 | Steps:   20 | Discriminator Loss: 0.7451 | Generator Loss: 2.0669
Epoch: 1/1 | Steps:   30 | Discriminator Loss: 0.7343 | Generator Loss: 2.0566
Epoch: 1/1 | Steps:   40 | Discriminator Loss: 0.6484 | Generator Loss: 2.5880
Epoch: 1/1 | Steps:   50 | Discriminator Loss: 0.7828 | Generator Loss: 1.9411
Epoch: 1/1 | Steps:   60 | Discriminator Loss: 0.7149 | Generator Loss: 1.9247
Epoch: 1/1 | Steps:   70 | Discriminator Loss: 0.6717 | Generator Loss: 3.6755
Epoch: 1/1 | Steps:   80 | Discriminator Loss: 1.1678 | Generator Loss: 0.7488
Epoch: 1/1 | Steps:   90 | Discriminator Loss: 1.4225 | Generator Loss: 0.7985
Epoch: 1/1 | Steps:  100 | Discriminator Loss: 1.0286 | Generator Loss: 1.5451
Epoch: 1/1 | Steps:  110 | Discriminator Loss: 1.1056 | Generator Loss: 1.0178
Epoch: 1/1 | Steps:  120 | Discriminator Loss: 0.9084 | Generator Loss: 2.2018
Epoch: 1/1 | Steps:  130 | Discriminator Loss: 0.8344 | Generator Loss: 1.2324
Epoch: 1/1 | Steps:  140 | Discriminator Loss: 0.8539 | Generator Loss: 1.8661
Epoch: 1/1 | Steps:  150 | Discriminator Loss: 0.8697 | Generator Loss: 1.3278
Epoch: 1/1 | Steps:  160 | Discriminator Loss: 1.6143 | Generator Loss: 0.4633
Epoch: 1/1 | Steps:  170 | Discriminator Loss: 1.0249 | Generator Loss: 1.0205
Epoch: 1/1 | Steps:  180 | Discriminator Loss: 0.9096 | Generator Loss: 1.4093
Epoch: 1/1 | Steps:  190 | Discriminator Loss: 1.2074 | Generator Loss: 0.6030
Epoch: 1/1 | Steps:  200 | Discriminator Loss: 1.0528 | Generator Loss: 1.2706
Epoch: 1/1 | Steps:  210 | Discriminator Loss: 1.0045 | Generator Loss: 1.1722
Epoch: 1/1 | Steps:  220 | Discriminator Loss: 1.0600 | Generator Loss: 3.3637
Epoch: 1/1 | Steps:  230 | Discriminator Loss: 1.2595 | Generator Loss: 0.7164
Epoch: 1/1 | Steps:  240 | Discriminator Loss: 1.4365 | Generator Loss: 3.1528
Epoch: 1/1 | Steps:  250 | Discriminator Loss: 1.0385 | Generator Loss: 0.9173
Epoch: 1/1 | Steps:  260 | Discriminator Loss: 0.7920 | Generator Loss: 2.1869
Epoch: 1/1 | Steps:  270 | Discriminator Loss: 1.8111 | Generator Loss: 3.8590
Epoch: 1/1 | Steps:  280 | Discriminator Loss: 1.0574 | Generator Loss: 2.1806
Epoch: 1/1 | Steps:  290 | Discriminator Loss: 1.0212 | Generator Loss: 2.5822
Epoch: 1/1 | Steps:  300 | Discriminator Loss: 1.1667 | Generator Loss: 0.7953
Epoch: 1/1 | Steps:  310 | Discriminator Loss: 0.9659 | Generator Loss: 1.7279
Epoch: 1/1 | Steps:  320 | Discriminator Loss: 1.0390 | Generator Loss: 1.0975
Epoch: 1/1 | Steps:  330 | Discriminator Loss: 0.9063 | Generator Loss: 1.0317
Epoch: 1/1 | Steps:  340 | Discriminator Loss: 0.7992 | Generator Loss: 1.4928
Epoch: 1/1 | Steps:  350 | Discriminator Loss: 0.8101 | Generator Loss: 1.5997
Epoch: 1/1 | Steps:  360 | Discriminator Loss: 1.2712 | Generator Loss: 0.6898
Epoch: 1/1 | Steps:  370 | Discriminator Loss: 1.1180 | Generator Loss: 0.8143
Epoch: 1/1 | Steps:  380 | Discriminator Loss: 1.1798 | Generator Loss: 1.4582
Epoch: 1/1 | Steps:  390 | Discriminator Loss: 0.9600 | Generator Loss: 1.7205
Epoch: 1/1 | Steps:  400 | Discriminator Loss: 0.9769 | Generator Loss: 1.1380
Epoch: 1/1 | Steps:  410 | Discriminator Loss: 0.9023 | Generator Loss: 1.7659
Epoch: 1/1 | Steps:  420 | Discriminator Loss: 1.1381 | Generator Loss: 0.7386
Epoch: 1/1 | Steps:  430 | Discriminator Loss: 0.8014 | Generator Loss: 1.6221
Epoch: 1/1 | Steps:  440 | Discriminator Loss: 1.0835 | Generator Loss: 1.3287
Epoch: 1/1 | Steps:  450 | Discriminator Loss: 0.8737 | Generator Loss: 2.0687
Epoch: 1/1 | Steps:  460 | Discriminator Loss: 0.9979 | Generator Loss: 1.2062
Epoch: 1/1 | Steps:  470 | Discriminator Loss: 1.0229 | Generator Loss: 1.2189
Epoch: 1/1 | Steps:  480 | Discriminator Loss: 1.3792 | Generator Loss: 0.7184
Epoch: 1/1 | Steps:  490 | Discriminator Loss: 1.3492 | Generator Loss: 0.5702
Epoch: 1/1 | Steps:  500 | Discriminator Loss: 0.8587 | Generator Loss: 1.2583
Epoch: 1/1 | Steps:  510 | Discriminator Loss: 1.0250 | Generator Loss: 1.5140
Epoch: 1/1 | Steps:  520 | Discriminator Loss: 1.3487 | Generator Loss: 0.7576
Epoch: 1/1 | Steps:  530 | Discriminator Loss: 1.2197 | Generator Loss: 2.3364
Epoch: 1/1 | Steps:  540 | Discriminator Loss: 1.1752 | Generator Loss: 1.4667
Epoch: 1/1 | Steps:  550 | Discriminator Loss: 1.1505 | Generator Loss: 1.5171
Epoch: 1/1 | Steps:  560 | Discriminator Loss: 1.6240 | Generator Loss: 2.3004
Epoch: 1/1 | Steps:  570 | Discriminator Loss: 1.1181 | Generator Loss: 0.8684
Epoch: 1/1 | Steps:  580 | Discriminator Loss: 1.2401 | Generator Loss: 1.0875
Epoch: 1/1 | Steps:  590 | Discriminator Loss: 1.1233 | Generator Loss: 0.7465
Epoch: 1/1 | Steps:  600 | Discriminator Loss: 1.0656 | Generator Loss: 1.5263
Epoch: 1/1 | Steps:  610 | Discriminator Loss: 1.2558 | Generator Loss: 0.8764
Epoch: 1/1 | Steps:  620 | Discriminator Loss: 1.4671 | Generator Loss: 0.5936
Epoch: 1/1 | Steps:  630 | Discriminator Loss: 1.0623 | Generator Loss: 0.9944
Epoch: 1/1 | Steps:  640 | Discriminator Loss: 1.1325 | Generator Loss: 1.2116
Epoch: 1/1 | Steps:  650 | Discriminator Loss: 1.1450 | Generator Loss: 1.8854
Epoch: 1/1 | Steps:  660 | Discriminator Loss: 1.1278 | Generator Loss: 0.8892
Epoch: 1/1 | Steps:  670 | Discriminator Loss: 1.2031 | Generator Loss: 1.0024
Epoch: 1/1 | Steps:  680 | Discriminator Loss: 1.0477 | Generator Loss: 1.3829
Epoch: 1/1 | Steps:  690 | Discriminator Loss: 1.2240 | Generator Loss: 1.4908
Epoch: 1/1 | Steps:  700 | Discriminator Loss: 1.4405 | Generator Loss: 0.6719
Epoch: 1/1 | Steps:  710 | Discriminator Loss: 1.0125 | Generator Loss: 1.0754
Epoch: 1/1 | Steps:  720 | Discriminator Loss: 1.2173 | Generator Loss: 0.8357
Epoch: 1/1 | Steps:  730 | Discriminator Loss: 0.9842 | Generator Loss: 1.3493
Epoch: 1/1 | Steps:  740 | Discriminator Loss: 1.2527 | Generator Loss: 0.8749
Epoch: 1/1 | Steps:  750 | Discriminator Loss: 0.9992 | Generator Loss: 1.1077
Epoch: 1/1 | Steps:  760 | Discriminator Loss: 1.1544 | Generator Loss: 1.6029
Epoch: 1/1 | Steps:  770 | Discriminator Loss: 1.4441 | Generator Loss: 1.2729
Epoch: 1/1 | Steps:  780 | Discriminator Loss: 1.0936 | Generator Loss: 0.9429
Epoch: 1/1 | Steps:  790 | Discriminator Loss: 1.1532 | Generator Loss: 1.0197
Epoch: 1/1 | Steps:  800 | Discriminator Loss: 1.0936 | Generator Loss: 1.1591
Epoch: 1/1 | Steps:  810 | Discriminator Loss: 1.2120 | Generator Loss: 1.0171
Epoch: 1/1 | Steps:  820 | Discriminator Loss: 1.1084 | Generator Loss: 1.5051
Epoch: 1/1 | Steps:  830 | Discriminator Loss: 1.0887 | Generator Loss: 1.3380
Epoch: 1/1 | Steps:  840 | Discriminator Loss: 1.1010 | Generator Loss: 0.8295
Epoch: 1/1 | Steps:  850 | Discriminator Loss: 1.2129 | Generator Loss: 0.8762
Epoch: 1/1 | Steps:  860 | Discriminator Loss: 1.3578 | Generator Loss: 0.6003
Epoch: 1/1 | Steps:  870 | Discriminator Loss: 1.2475 | Generator Loss: 1.2602
Epoch: 1/1 | Steps:  880 | Discriminator Loss: 1.1522 | Generator Loss: 1.1371
Epoch: 1/1 | Steps:  890 | Discriminator Loss: 1.1640 | Generator Loss: 1.2570
Epoch: 1/1 | Steps:  900 | Discriminator Loss: 1.1373 | Generator Loss: 1.1389
Epoch: 1/1 | Steps:  910 | Discriminator Loss: 1.3408 | Generator Loss: 0.8507
Epoch: 1/1 | Steps:  920 | Discriminator Loss: 1.2309 | Generator Loss: 1.0933
Epoch: 1/1 | Steps:  930 | Discriminator Loss: 1.2344 | Generator Loss: 0.9619
Epoch: 1/1 | Steps:  940 | Discriminator Loss: 1.1704 | Generator Loss: 0.9632
Epoch: 1/1 | Steps:  950 | Discriminator Loss: 1.3185 | Generator Loss: 1.0433
Epoch: 1/1 | Steps:  960 | Discriminator Loss: 1.2230 | Generator Loss: 0.8555
Epoch: 1/1 | Steps:  970 | Discriminator Loss: 1.1298 | Generator Loss: 0.8632
Epoch: 1/1 | Steps:  980 | Discriminator Loss: 1.2727 | Generator Loss: 0.7010
Epoch: 1/1 | Steps:  990 | Discriminator Loss: 1.2516 | Generator Loss: 0.8920
Epoch: 1/1 | Steps: 1000 | Discriminator Loss: 1.1760 | Generator Loss: 1.0956
Epoch: 1/1 | Steps: 1010 | Discriminator Loss: 1.2017 | Generator Loss: 0.6799
Epoch: 1/1 | Steps: 1020 | Discriminator Loss: 1.1682 | Generator Loss: 1.0266
Epoch: 1/1 | Steps: 1030 | Discriminator Loss: 1.2723 | Generator Loss: 0.7197
Epoch: 1/1 | Steps: 1040 | Discriminator Loss: 1.3668 | Generator Loss: 0.7889
Epoch: 1/1 | Steps: 1050 | Discriminator Loss: 1.1842 | Generator Loss: 1.0268
Epoch: 1/1 | Steps: 1060 | Discriminator Loss: 1.2052 | Generator Loss: 0.8421
Epoch: 1/1 | Steps: 1070 | Discriminator Loss: 1.2338 | Generator Loss: 1.0989
Epoch: 1/1 | Steps: 1080 | Discriminator Loss: 1.3356 | Generator Loss: 0.9173
Epoch: 1/1 | Steps: 1090 | Discriminator Loss: 1.2787 | Generator Loss: 0.7436
Epoch: 1/1 | Steps: 1100 | Discriminator Loss: 1.0622 | Generator Loss: 1.1194
Epoch: 1/1 | Steps: 1110 | Discriminator Loss: 1.2558 | Generator Loss: 1.1433
Epoch: 1/1 | Steps: 1120 | Discriminator Loss: 1.2195 | Generator Loss: 0.7202
Epoch: 1/1 | Steps: 1130 | Discriminator Loss: 1.2240 | Generator Loss: 0.9324
Epoch: 1/1 | Steps: 1140 | Discriminator Loss: 1.1998 | Generator Loss: 0.7381
Epoch: 1/1 | Steps: 1150 | Discriminator Loss: 1.1327 | Generator Loss: 0.8767
Epoch: 1/1 | Steps: 1160 | Discriminator Loss: 1.2732 | Generator Loss: 0.8967
Epoch: 1/1 | Steps: 1170 | Discriminator Loss: 0.9616 | Generator Loss: 1.4207
Epoch: 1/1 | Steps: 1180 | Discriminator Loss: 1.2190 | Generator Loss: 0.7477
Epoch: 1/1 | Steps: 1190 | Discriminator Loss: 1.2540 | Generator Loss: 0.7425
Epoch: 1/1 | Steps: 1200 | Discriminator Loss: 1.2058 | Generator Loss: 0.7495
Epoch: 1/1 | Steps: 1210 | Discriminator Loss: 1.2819 | Generator Loss: 0.8361
Epoch: 1/1 | Steps: 1220 | Discriminator Loss: 1.1230 | Generator Loss: 0.9453
Epoch: 1/1 | Steps: 1230 | Discriminator Loss: 1.3331 | Generator Loss: 0.8725
Epoch: 1/1 | Steps: 1240 | Discriminator Loss: 1.2527 | Generator Loss: 0.9538
Epoch: 1/1 | Steps: 1250 | Discriminator Loss: 1.0701 | Generator Loss: 0.9003
Epoch: 1/1 | Steps: 1260 | Discriminator Loss: 1.1275 | Generator Loss: 1.1143
Epoch: 1/1 | Steps: 1270 | Discriminator Loss: 1.2247 | Generator Loss: 0.6980
Epoch: 1/1 | Steps: 1280 | Discriminator Loss: 1.3715 | Generator Loss: 0.5941
Epoch: 1/1 | Steps: 1290 | Discriminator Loss: 1.4255 | Generator Loss: 0.6279
Epoch: 1/1 | Steps: 1300 | Discriminator Loss: 1.1990 | Generator Loss: 0.9635
Epoch: 1/1 | Steps: 1310 | Discriminator Loss: 1.3098 | Generator Loss: 1.1692
Epoch: 1/1 | Steps: 1320 | Discriminator Loss: 1.1232 | Generator Loss: 1.5710
Epoch: 1/1 | Steps: 1330 | Discriminator Loss: 1.2317 | Generator Loss: 0.9482
Epoch: 1/1 | Steps: 1340 | Discriminator Loss: 1.1999 | Generator Loss: 0.9347
Epoch: 1/1 | Steps: 1350 | Discriminator Loss: 1.1197 | Generator Loss: 0.9032
Epoch: 1/1 | Steps: 1360 | Discriminator Loss: 1.2076 | Generator Loss: 0.7876
Epoch: 1/1 | Steps: 1370 | Discriminator Loss: 1.0578 | Generator Loss: 1.0952
Epoch: 1/1 | Steps: 1380 | Discriminator Loss: 1.1673 | Generator Loss: 0.9155
Epoch: 1/1 | Steps: 1390 | Discriminator Loss: 1.0591 | Generator Loss: 1.2697
Epoch: 1/1 | Steps: 1400 | Discriminator Loss: 1.1417 | Generator Loss: 0.9264
Epoch: 1/1 | Steps: 1410 | Discriminator Loss: 0.9707 | Generator Loss: 1.5016
Epoch: 1/1 | Steps: 1420 | Discriminator Loss: 1.1970 | Generator Loss: 0.9356
Epoch: 1/1 | Steps: 1430 | Discriminator Loss: 1.3483 | Generator Loss: 0.6548
Epoch: 1/1 | Steps: 1440 | Discriminator Loss: 1.2612 | Generator Loss: 0.9495
Epoch: 1/1 | Steps: 1450 | Discriminator Loss: 1.0996 | Generator Loss: 0.9764
Epoch: 1/1 | Steps: 1460 | Discriminator Loss: 1.1755 | Generator Loss: 0.8682
Epoch: 1/1 | Steps: 1470 | Discriminator Loss: 1.2388 | Generator Loss: 0.9291
Epoch: 1/1 | Steps: 1480 | Discriminator Loss: 1.1775 | Generator Loss: 0.9340
Epoch: 1/1 | Steps: 1490 | Discriminator Loss: 1.1962 | Generator Loss: 1.2550
Epoch: 1/1 | Steps: 1500 | Discriminator Loss: 1.3084 | Generator Loss: 1.2019
Epoch: 1/1 | Steps: 1510 | Discriminator Loss: 1.0083 | Generator Loss: 1.1513
Epoch: 1/1 | Steps: 1520 | Discriminator Loss: 1.1820 | Generator Loss: 0.7867
Epoch: 1/1 | Steps: 1530 | Discriminator Loss: 1.0672 | Generator Loss: 0.9465
Epoch: 1/1 | Steps: 1540 | Discriminator Loss: 1.1528 | Generator Loss: 1.0137
Epoch: 1/1 | Steps: 1550 | Discriminator Loss: 1.3336 | Generator Loss: 0.8832
Epoch: 1/1 | Steps: 1560 | Discriminator Loss: 1.2636 | Generator Loss: 0.8560
Epoch: 1/1 | Steps: 1570 | Discriminator Loss: 1.1777 | Generator Loss: 0.9765
Epoch: 1/1 | Steps: 1580 | Discriminator Loss: 1.1602 | Generator Loss: 0.7968
Epoch: 1/1 | Steps: 1590 | Discriminator Loss: 1.2886 | Generator Loss: 0.6261
Epoch: 1/1 | Steps: 1600 | Discriminator Loss: 1.3002 | Generator Loss: 0.8905
Epoch: 1/1 | Steps: 1610 | Discriminator Loss: 1.2338 | Generator Loss: 0.6545
Epoch: 1/1 | Steps: 1620 | Discriminator Loss: 1.0786 | Generator Loss: 1.2606
Epoch: 1/1 | Steps: 1630 | Discriminator Loss: 1.2211 | Generator Loss: 0.7576
Epoch: 1/1 | Steps: 1640 | Discriminator Loss: 1.1794 | Generator Loss: 0.8307
Epoch: 1/1 | Steps: 1650 | Discriminator Loss: 1.0331 | Generator Loss: 0.9554
Epoch: 1/1 | Steps: 1660 | Discriminator Loss: 1.2954 | Generator Loss: 0.8543
Epoch: 1/1 | Steps: 1670 | Discriminator Loss: 1.1463 | Generator Loss: 0.7405
Epoch: 1/1 | Steps: 1680 | Discriminator Loss: 1.2085 | Generator Loss: 1.0619
Epoch: 1/1 | Steps: 1690 | Discriminator Loss: 1.0967 | Generator Loss: 0.9880
Epoch: 1/1 | Steps: 1700 | Discriminator Loss: 1.1860 | Generator Loss: 0.8745
Epoch: 1/1 | Steps: 1710 | Discriminator Loss: 1.1017 | Generator Loss: 1.3963
Epoch: 1/1 | Steps: 1720 | Discriminator Loss: 1.2579 | Generator Loss: 0.8581
Epoch: 1/1 | Steps: 1730 | Discriminator Loss: 1.1136 | Generator Loss: 0.9337
Epoch: 1/1 | Steps: 1740 | Discriminator Loss: 1.1068 | Generator Loss: 0.7985
Epoch: 1/1 | Steps: 1750 | Discriminator Loss: 1.2035 | Generator Loss: 0.7916
Epoch: 1/1 | Steps: 1760 | Discriminator Loss: 1.2851 | Generator Loss: 0.8280
Epoch: 1/1 | Steps: 1770 | Discriminator Loss: 1.3243 | Generator Loss: 0.9431
Epoch: 1/1 | Steps: 1780 | Discriminator Loss: 1.2255 | Generator Loss: 0.8921
Epoch: 1/1 | Steps: 1790 | Discriminator Loss: 1.3842 | Generator Loss: 1.5278
Epoch: 1/1 | Steps: 1800 | Discriminator Loss: 1.1246 | Generator Loss: 1.1185
Epoch: 1/1 | Steps: 1810 | Discriminator Loss: 1.1151 | Generator Loss: 1.0883
Epoch: 1/1 | Steps: 1820 | Discriminator Loss: 1.2217 | Generator Loss: 0.9495
Epoch: 1/1 | Steps: 1830 | Discriminator Loss: 1.3340 | Generator Loss: 1.0713
Epoch: 1/1 | Steps: 1840 | Discriminator Loss: 1.1289 | Generator Loss: 0.9672
Epoch: 1/1 | Steps: 1850 | Discriminator Loss: 1.1974 | Generator Loss: 0.8835
Epoch: 1/1 | Steps: 1860 | Discriminator Loss: 1.1048 | Generator Loss: 1.0621
Epoch: 1/1 | Steps: 1870 | Discriminator Loss: 1.2648 | Generator Loss: 0.6668
Epoch: 1/1 | Steps: 1880 | Discriminator Loss: 1.1952 | Generator Loss: 1.0951
Epoch: 1/1 | Steps: 1890 | Discriminator Loss: 1.2083 | Generator Loss: 0.7488
Epoch: 1/1 | Steps: 1900 | Discriminator Loss: 1.3580 | Generator Loss: 0.6624
Epoch: 1/1 | Steps: 1910 | Discriminator Loss: 1.1891 | Generator Loss: 1.1623
Epoch: 1/1 | Steps: 1920 | Discriminator Loss: 1.1508 | Generator Loss: 1.1633
Epoch: 1/1 | Steps: 1930 | Discriminator Loss: 1.2947 | Generator Loss: 0.6497
Epoch: 1/1 | Steps: 1940 | Discriminator Loss: 1.1230 | Generator Loss: 0.8929
Epoch: 1/1 | Steps: 1950 | Discriminator Loss: 1.2238 | Generator Loss: 0.8799
Epoch: 1/1 | Steps: 1960 | Discriminator Loss: 1.2741 | Generator Loss: 0.7679
Epoch: 1/1 | Steps: 1970 | Discriminator Loss: 1.4580 | Generator Loss: 0.5232
Epoch: 1/1 | Steps: 1980 | Discriminator Loss: 1.2075 | Generator Loss: 0.9308
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Epoch: 1/1 | Steps: 6330 | Discriminator Loss: 1.3253 | Generator Loss: 0.6748

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.